1. Field of the Invention
The present invention relates to medical imaging systems, and more specifically to medical imaging systems for use in angiography, for example.
2. Background of the Invention
A stenosis in a blood vessel, for example, an artery refers to narrowing of the artery lumen due to plaque formation on the interior wall of the artery. The severity of the narrowing depends upon the amount of cross-sectional area of the lumen that is occluded by plaque. While narrowing of the arteries may occur in any artery of the body (e.g., carotid arteries), particular concern has been placed on investigating the narrowing of arteries of the heart, the coronary arteries (coronary heart disease), since narrowing of these arteries is one of the primary causes of heart attacks. Accordingly, coronary angiography refers to the process (and associated systems) of investigating coronary arteries to determine the severity of any narrowing (i.e., to find stenotic arteries) that may exist.
To image the arteries, a catheter is inserted into an artery of the arm or leg of a patient, where it is eventually advanced into the coronary arteries. Upon arriving at the coronary arteries, a radio-opaque substance is injected therein, so that the arteries may be imaged, using, for example, an X-ray angiography system.
The system takes “snapshots” (i.e., angiographic cine-runs) of the arteries at several different perspectives, to obtain complete views of the one or more arterial networks being investigated. Also, since narrowing is often asymmetrical about the axis of the artery, it is necessary to obtain at least two images, and preferably more, preferably perpendicular to an artery's axis from different perspectives (preferably orthogonal perspectives) to assess the severity of a stenosis. However, it is generally very difficult to obtain purely perpendicular perspectives of the vessels.
Accordingly, determination of the perspective positions is partially arbitrary and partially a process of trial and error (once a stenosis has been observed). However, the overall number of images that can be obtained is limited by time, safety and cost. Usually four to seven projections for the left coronary arterial system and two to four images for the right arterial system are obtained.
An operator of an angiography system assesses the severity of a stenosis in the coronary arteries either on the basis of visual examination of a plurality of images (projections) or by computer analysis of a single image. As indicated above, since most of the images are, in general, not purely perpendicular to the arterial axis, estimation of stenosis severity is usually not accurate by either means.
Currently, there exist two-dimensional (2D) Quantitative Coronary Angiography (QCA) systems, which create 2D images of vessels for the investigation of stenoses, as well as three-dimensional (3D) QCA methods which also create a 3D reconstruction (3DR) of an entire arterial tree for investigation of stenotic vessels.
The 2D QCA systems basically implement the following steps: import of a specific image, vessel extraction for this image and then QCA for the vessel of interest. 2D QCA systems usually provide diameter based analysis of the lesion and not densitometry analysis. In some cases, densitometry analysis is provided via the usage of DSA, but not for scenes that include motion, like the coronaries.
The 3D QCA methods generally include the following steps: image acquisition, vessel extraction from the 2D projections. The 3D QCA systems additionally include imaging geometry recovery, point-by-point matching (between images) and, of course, 3DR. The QCA of the 3D system generally includes, morphology assessment (including vessel foreshortening, overlapping, angulation, tortuosity), and in some cases measurements, usually true length and diameter information. However, cross-section area measurements are rarely addressed, although attempts have been made to achieve a precise representation of cross section profile along the vessels. A method based on some heuristics in a framework of algebraic reconstruction approach was suggested in U.S. Pat. No. 6,301,498 to Greenberg. However this method requires a special arrangement of at least four (4) acquisitions from different directions orthogonal to the artery.
Also, in both 2D and 3D QCA systems and methods, one important aspect of measurements and stenosis severity is the establishment of healthy vessel measurements. Systems and methods that present healthy vessel (or related) measurements use, for example, the interpolation of values based on measured diameters at proximal and at distal portions. This step is critical, since it is a basis for many measurements. At the same time, this step is very sensitive and could easily produce incorrect measurements.
Other problems exist with reference to the methods for the existing 3D imaging systems. For example, with image acquisition, prior art systems utilize either bi-plane acquisition, rotational acquisition, or single projection (image) acquisition (the most general approach (see U.S. Pat. Nos. 6,047,080 and 6,169,917). Although bi-plane acquisition minimizes distortions due to cardiac cycle phase, the technique is insufficient in some situations of epi-polar geometry ambiguity. With regard to rotational acquisition systems, although close in time, these systems do not solve either a cardiac phase problem or the epi-polar geometry ambiguity.
With regard to imaging geometry recovery, the number of control points needed for geometry recovery depends on the type of transformation that is found and assumptions on unknown parameters. Accordingly, the number of control points can range anywhere from five (5) (see, for example, U.S. Pat. Nos. 6,047,080 and 6,501,848) to eight (8) (see, for example, U.S. Pat. No. 4,875,165) for perspective transformation. However, the confident and accurate identification of at least five corresponding points on multiple images is a burdensome procedure, if at all possible, since, for example, the right coronary artery system often lacks adequate branching points.
Moreover, whether non-linear or linear optimization is used, both solutions suffer from an instability problem. Specifically, the natural candidate points to serve as control points are the branching points in the arterial tree. However, it is very often the case that the precise location of a branching point is difficult to identify due to that area of the arterial tree overlapping another vessel or itself Moreover, as usual in computational geometry, not every required set of points is useful to produce the transformation. For example, if all the points lie on a common line in an image, the points can not serve for transformation calculation. Finally, transformation to 3DR from a family of perspective transformations, in general, can not compensate for local distortions in each image caused by the image being taken at different phase of either the heart cycle and patient respiration (for example).
There also exist a variety of techniques for vessel extraction in prior art imaging systems from 2D X-ray angiographic images. However, the ability to perform vessel extraction in clinical practice relates to the degree of automation and robustness of a particular process. For example, in U.S. Pat. No. 6,047,080, an operator must input six (6) marking points to identify six (6) branches of an artery tree in each image, and make several clicks per branch to define an initial centerline of each branch in every image. In addition, in order to stabilize the solution, the operator is recommended to add control points of high curvature and add stenosis points.
When the centerlines representing the entire vascular tree (including various branches) in 2D projections have been extracted, point-by-point matching utilizes (e.g., for multiple images) the epi-polar principle. Epi-polar geometry is premised on the statement that for an imaged 3D point, its projections on a pair of images and two (2) associated focal points belong to a common (epi-polar) plane. Accordingly, for any given point on one image, the search for the corresponding point on another image may be found on the epi-polar line (intersection of the epipolar plane with the image plane). However, this approach yields sufficient results only if. (i) the imaging geometry model adequately relates the organ and its 2D image, and (ii) the imaged vessel does not change its shape between the image acquisitions. This is why, in clinical practice, the restrictions of the straightforward epi-polar geometry approach are very limiting in tenns of accuracy and quality of the 3D model.
In view of the above-mentioned short comings of the prior art, current 2D QCA systems do not deliver sufficient support for coronary angiography (for example) and current 3D QCA systems are not in clinical use since these systems either deliver incorrect results or are too cumbersome to use.
Thus, there exists a need for a 3DR system which can be used in clinical procedures (e.g., angiography) that delivers a system that may include a practical, intuitive, easy-to-use, robust solution to overcome at least one and preferably all of the above-mentioned disadvantages of the prior art systems and methods.